This invention relates generally to systems that collect and process high resolution amplitude data, but ultimately use relatively low resolution amplitude data because of computer processing constraint considerations. In particular, this invention is directed to increasing the effective sampling capacity of the collection and processing system without increasing the sophistication of the data collection device.
A high resolution amplitude data collection device generally samples signal amplitude information through an array of elements that each converts incident energy (which may be a light wave) into analog signals. The resulting analog signals are generally digitized for further processing by a computer. An image processing device, for example, produces files that represent gray scale values of each pixel within the image. Thresholding or quantizing techniques are frequently used to reduce image data down to elements that are simpler to work with and smaller in size, so as to increase processing speed.
Image post-processing techniques, for example spatial up-sampling of a gray level file prior to performing thresholding or quantizing, are typically used with copiers and flatbed scanning devices. These techniques tend to improve the quality of the quantized image.
Spatial up-sampling refers to a process for mathematically generating the probable values of higher resolution digital information in a data stream. In the field of image processing, spatial up-sampling increases the resolution of the features in an image by inserting interpolated intermediate values.
Signal quantizing is a technique for processing data by identifying and extracting mathematically-defined features. Quantization of the high resolution amplitude data reduces the information volume to a manageable size to make electronic processing feasible. For example, an 8-bit image has 256 different gray levels. Typically the number of gray levels is reduced by a process known as binarization, a form of quantization. In some methods, the gray level values of eight pixels surrounding each pixel in the image are evaluated and a simple thresholding scheme is used. Thresholding is a process that involves taking the difference between the gray-level value of the middle pixel and the surrounding pixels, and then marking the position of the middle pixel in a resulting array with a gray level value of either 0 (difference equal or below the threshold) or 1 (difference above a certain threshold). The resulting array highlights the features of interest, as well as allowing the packing and compression of data to a significantly smaller size.
What is needed is a data collection acceleration system that performs asynchronous and real-time spatial up-sampling and amplitude quantizing for live operations, such as when a camera images parcels rapidly traveling past the camera on a conveyor belt. The ideal data collection acceleration system would also allow flexible selection of spatial up-sampling and amplitude quantizing techniques, based on the known optimal performance of such algorithms on particular inputs. A data collection acceleration system that results from these improvements could increase the spatial and temporal capacity, herein referred to as the scan resolution, of the collection device without upgrading the collection equipment and without decreasing the overall performance of the data processing system.